CN112506975A - Information recommendation method, device and system - Google Patents
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Abstract
An information recommendation method, device and system can achieve the purpose of obtaining user related data; processing the acquired user related data to obtain user standardized data; reading data in a database; calculating the obtained user standardized data into user tag data based on the relationship data of the user tag data and the user standardized data in the read database; roughly matching the user tag data to obtain a recommended content alternative pool which is preliminarily recommended to the user; sending the recommended content alternative pool to a user, and performing accurate matching based on the preference of the user to obtain final recommended content; and sending the final recommended content to a client. Based on the method, the device and the system, the systematic personalized recommendation solution can be obtained based on various comprehensive information.
Description
Technical Field
The present invention relates to the field of information processing technologies, and in particular, to an information recommendation method, apparatus, and system.
Background
With the rapid development of internet and mobile communication technology, various information devices have been produced, and information which is explosively increased is also full of various information devices used by users. In order to enable users to obtain information of interest of the users, personalized information recommendation becomes one of indispensable services in network life of people and also becomes a key point of future development of internet products.
Particularly, in the field of early education of infants, particularly in the field of early education of infants of 0-3 years old, infant care information in the market is very much, but the readability of various infant care information is not high, various contents are disclosed in a popular way (32429), the professionality is difficult to guarantee, and the information is not beneficial to a family member to reasonably utilize to nurture the infants. Although some personalized early education scheme recommendation methods are available in the market, various user information is only divided and recommended based on the characteristics of the divided user information, and a comprehensive scheme for solving the problems cannot be formed. For example, there are game recommendations made according to the test questions provided by the user, toy recommendations made according to the infant's month-old age, and course recommendations made according to the infant's month-old age in the market, but these segmented, fragmented recommendations cannot be given to parents ' specific guidance and specific tools in view of the great individualization of the development of infants between 0 and 3 years old.
Therefore, in the field of the existing personalized early education, a solution for obtaining a system including courses, teaching aids and assessment according to the family condition, the development condition of infants and other comprehensive information is urgently needed.
Disclosure of Invention
The invention aims to provide an information recommendation method, device and system, so that a systematic personalized recommendation solution can be obtained based on various comprehensive information.
According to one aspect of the present invention, there is provided an information recommendation method, the method including the steps of:
s1: acquiring user related data;
s2: processing the acquired user related data to obtain user standardized data;
s3: reading data in a database;
s4: calculating the obtained user standardized data into user tag data based on the relationship data of the user tag data and the user standardized data in the read database;
s5: roughly matching the user tag data to obtain a recommended content alternative pool which is preliminarily recommended to the user;
s6: sending the recommended content alternative pool to a user, and performing accurate matching based on the preference of the user to obtain final recommended content;
s7: and sending the final recommended content to a client.
Further, the acquiring the user-related data specifically includes: information related to the user and information related to the infant are obtained.
Further, the acquiring information related to the user and information related to the infant specifically includes:
acquiring infant ability development data information input by a user for answering a test question, wherein the infant ability development data information comprises family basic information of the user;
acquiring information related to a user and an infant, wherein the information is communicated between after-sales service personnel and the user;
acquiring behavior data of a user on a network or an application program;
and acquiring product use feedback information or product use evaluation information submitted by a user on the equipment.
Further, the processing the obtained user-related data to obtain user standardized data specifically includes: mapping the obtained user-related data to user standardized data based on a built-in data dictionary.
Further, the data in the database includes:
(1) recommending content tag data;
(2) user tag data;
(3) relational data of user tag data and user standardized data;
(4) and relationship data of the user tag data and the recommended content tag data.
Further, the recommended content label data includes an infant ability label, a teaching aid label, a course content label, and a nursing information content label.
Further, the user tag data includes a user attribute tag and a user behavior tag.
Further, the step of roughly matching the user tag data to obtain a recommended content alternative pool preliminarily recommended to the user specifically includes:
and based on the relation data of the user tag data and the recommended content tag data in the read database, roughly matching the user tag data to obtain a recommended content alternative pool which is preliminarily recommended to the user.
Further, the final recommended content comprises infant ability reports, teaching aid content, course content and child care information content.
According to another aspect of the present invention, there is provided an information recommendation apparatus, the apparatus including: the device comprises an acquisition unit, a processing unit, a reading unit, a calculation unit, a rough matching unit, an accurate matching unit and an output unit;
the acquisition unit is used for acquiring user related data;
the processing unit is used for processing the acquired user related data to acquire user standardized data;
the reading unit is used for reading data in a database;
the calculation unit is used for calculating the obtained user standardized data into user tag data based on the relationship data of the user tag data and the user standardized data in the read database;
the rough matching unit is used for roughly matching the user tag data to obtain a recommended content alternative pool which is preliminarily recommended to the user;
the accurate matching unit is used for sending the recommended content alternative pool to the user and carrying out accurate matching based on the preference of the user to obtain the final recommended content;
and the output unit is used for sending the final recommended content to a client.
Further, the acquiring the user-related data specifically includes: information related to the user and information related to the infant are obtained.
Further, the acquiring information related to the user and information related to the infant specifically includes:
acquiring infant ability development data information input by a user for answering a test question, wherein the infant ability development data information comprises family basic information of the user;
acquiring information related to a user and an infant, wherein the information is communicated between after-sales service personnel and the user;
acquiring behavior data of a user on a network or an application program;
and acquiring product use feedback information or product use evaluation information submitted by a user on the equipment.
Further, the processing the obtained user-related data to obtain user standardized data specifically includes: mapping the obtained user-related data to user standardized data based on a built-in data dictionary.
Further, the data in the database includes:
(1) recommending content tag data;
(2) user tag data;
(3) relational data of user tag data and user standardized data;
(4) and relationship data of the user tag data and the recommended content tag data.
Further, the recommended content label data includes an infant ability label, a teaching aid label, a course content label, and a nursing information content label.
Further, the user tag data includes a user attribute tag and a user behavior tag.
Further, the step of roughly matching the user tag data to obtain a recommended content alternative pool preliminarily recommended to the user specifically includes:
and based on the relation data of the user tag data and the recommended content tag data in the read database, roughly matching the user tag data to obtain a recommended content alternative pool which is preliminarily recommended to the user.
Further, the final recommended content comprises infant ability reports, teaching aid content, course content and child care information content.
According to another aspect of the present invention, there is provided an information recommendation system including: the system comprises a client, an information recommendation device and a database;
the information recommendation device is used for acquiring user related data, processing the user related data to acquire user standardized data, calculating the user standardized data into user tag data based on the relation data of the user tag data read from the database and the user standardized data, roughly matching the user tag data to acquire a recommended content alternative pool preliminarily recommended to a user, sending the recommended content alternative pool to the client, accurately matching based on user preference to acquire final recommended content, and sending the final recommended content to the client;
the database stores the relation data of the user tag data and the user standardized data and the relation data of the user tag data and the recommended content tag data;
and the client receives the recommended content alternative pool sent by the information recommending device, sends the preference of the user to the information recommending device, and receives the final recommended content.
Further, the step of roughly matching the user tag data to obtain a recommended content alternative pool preliminarily recommended to the user specifically includes: and based on the read relation data between the user tag data and the recommended content tag data in the database system, roughly matching the user tag data to obtain a recommended content alternative pool which is preliminarily recommended to the user.
According to another aspect of the present invention, there is provided an electronic apparatus, comprising:
a storage device;
one or more processors;
wherein the storage device is configured to store one or more programs, which when executed by the one or more processors, cause the one or more processors to implement an information recommendation method as described above.
According to another aspect of the present invention, there is provided a computer-readable storage medium having stored thereon a computer program which, when executed, implements an information recommendation method as described above.
According to another aspect of the invention, the invention provides a computer program product comprising computer program instructions for implementing an information recommendation method as described above, when said instructions are executed by a processor.
Compared with the prior art, the invention has the following advantages: the invention can acquire various information about users and infants from various channels in advance as basic data for recommendation, obtain a recommended content alternative pool through the corresponding relation between the user label data and the recommended content label data which are established in advance, and further calculate the recommended information meeting the actual requirements of the users from the content alternative pool based on the preference selection of the users, thereby obtaining a systematic solution including courses, teaching aids and evaluation according to the comprehensive information of the family conditions of the users, the development conditions of the infants and the like.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a flowchart of an information recommendation method in an embodiment of the present invention;
FIG. 2 is a schematic diagram of tag data included in a database according to an embodiment of the present invention;
FIG. 3 is a diagram illustrating recommended content tag data in an embodiment of the invention;
FIG. 4 is a schematic diagram illustrating the components of an information recommendation apparatus according to an embodiment of the present invention;
FIG. 5 is a schematic diagram illustrating the components of a client according to an embodiment of the present invention;
fig. 6 is a schematic composition diagram of an information recommendation system in an embodiment of the present invention.
The same or similar reference numbers in the drawings identify the same or similar elements.
Detailed Description
In order to make the problems, technical solutions and technical effects to be solved by the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of embodiments of the present invention, and not all embodiments. All other embodiments and applications which can be obtained by a person skilled in the art based on the embodiments of the present invention without any inventive step are within the scope of the present invention.
Before describing exemplary embodiments of the present invention in more detail, it should be noted that although the flow charts of the present invention describe various operations as a sequential process, many of the operational steps can be performed in parallel or concurrently, and the order of the operations can be rearranged as desired. In addition, the process may be terminated when the operation is completed, but may have additional steps not included in the figure.
The client of the present invention includes various user devices or electronic devices including, but not limited to, computers, smart mobile phones, and PDAs. It should be noted that the user equipment, the electronic device, etc. are only examples, and other existing or future intelligent electronic devices may be applicable to the present invention, and are included in the scope of the present invention and are included by reference.
The following describes in further detail embodiments of the present invention with reference to the accompanying drawings.
The first embodiment is as follows:
referring to fig. 1, a first embodiment of the present invention provides an information recommendation method, including the following steps:
s1: and acquiring user related data.
The acquiring of the user related data specifically comprises: information related to the user and information related to the infant are obtained.
The acquiring information related to the user and information related to the infant specifically includes:
acquiring infant ability development data information input by a user for answering a test question, wherein the infant ability development data information comprises family basic information of the user;
acquiring information related to a user and an infant, wherein the information is communicated between after-sales service personnel and the user;
acquiring behavior data of a user on the network or an application program, and capturing and recording the behavior data through a data embedded point system on the network or the application program;
and acquiring product use feedback information or product use evaluation information submitted by a user on the equipment.
The behavior of the user such as class, card punching and the like is recorded by the system.
S2: the acquired user-related data is processed to obtain user-normalized data.
The step of processing the acquired user-related data to acquire user standardized data specifically includes: mapping the obtained user-related data to user standardized data based on a built-in data dictionary.
For data such as data autonomously entered in step S1 or behavior data generated during operation of the terminal device, the system maps the data to the data dictionary to obtain a standardized value, and after this process is completed, the data stored in the system is no longer scattered and discrete for subsequent use.
In the system of the invention, a data dictionary is pre-built, and for each user-related data, the data dictionary has corresponding numerical values corresponding to the numerical values, so that each user-related data can be mapped into corresponding user standardized numerical values by using the data dictionary. For example, for the gender of a baby, in the data dictionary, 1 is used to represent a male baby, 2 is used to represent a female baby, and 0 is used to represent unknown.
S3: data in the database is read.
Referring to fig. 2, the data in the database includes the following tag data:
(1) the content tag data is recommended.
Referring to fig. 3, the recommended content label data includes an infant ability label, a teaching aid label, a course content label, and a nursery information content label. The tags may be entered by a professional learner.
Where each of the tags can be ranked, the system defines nine large dimensions and about 60 fine categories under the nine large dimensions, and hundreds of capability points under the fine categories. For example, the three-layer label of ' whole brain thinking ' -sensory perception development-tactile experience ' represents the ability of ' tactile experience ' in the subdivision category of ' whole brain thinking ' of the infant in the large development education direction of ' whole brain thinking '.
(2) User tag data.
The user tag data includes user attribute tags and user behavior tags. The method specifically comprises the following steps:
a) the user attribute tags comprise a month age tag of a baby of the user, a gender tag of the baby, a child condition tag of a family of the user (such as full-time mothers and mothers, old people's help belts and the like), and the like;
b) user's action label, including using habit time interval distribution label, teaching aid preference label, course preference label etc..
(3) Relationship data of the user tag data and the user standardized data.
The system accumulates the relationship data of the user label and the user standardized data based on the historical user, and the specific label calculation process is shown in the following step S4.
(4) And relationship data of the user tag data and the recommended content tag data.
This part of data is an accumulation of data generated at the stages of step S5 and step S6 described below, and the relationship data of the user tag data and the recommended content tag data is continuously accumulated by the machine learning algorithm.
S4: and calculating the obtained user standardized data into user tag data based on the relationship data of the user tag data and the user standardized data in the read database.
In the process of system label classification, different calculation modes are adopted for different label types. For example, for the month age and sex labels, the system adopts an information collection and classification mode; for the use habit label, the system adopts a calculation mode of statistical distribution; and for the preference label of the teaching aid, the system adopts a machine learning calculation mode based on a KNN algorithm.
S5: and roughly matching the user tag data to obtain a recommended content alternative pool which is preliminarily recommended to the user.
The recommended content alternative pool comprises infant ability reports, teaching aids, course contents, infant care information contents and the like.
The step of roughly matching the user tag data to obtain a recommended content alternative pool preliminarily recommended to the user specifically includes: and based on the relation data of the user tag data and the recommended content tag data in the read database, roughly matching the user tag data to obtain a recommended content alternative pool which is preliminarily recommended to the user. The system preliminarily calculates a recommended content alternative pool recommended to the user based on the user tag data calculated in step S4 and the relationship data of the user tag data and the recommended content tag data read in step S3.
In the calculation process of the part, the system adopts various machine learning algorithms in advance, including but not limited to collaborative filtering and Knowledge-based recommendation algorithms (Knowledge-based Recommendations), and the recommended contents are sorted according to relevance.
S6: and sending the recommended content alternative pool to the user, and performing accurate matching based on the preference of the user to obtain the final recommended content.
The final recommended content comprises infant ability development reports, teaching aids, course content, infant care information content and the like.
The alternative pool calculated in step S5 may have more recommended contents than the end user can obtain, and may be different from the contents that the user wants to know, which results in not meeting the final expectation of the user, and the system may give the user a chance to select in the forward direction in the process, for example, the user may avoid obtaining a previously learned course or an already existing teaching aid at home by changing a professional teaching aid or course, or receive a subjective preference input by the user, and further calculate the final recommended contents based on the subjective preference of the user.
S7: and sending the final recommended content to a client.
And sending the finally recommended content to a client, and viewing and displaying the content by the client.
By means of the information recommendation method in the embodiment, various information about users and infants can be acquired from various channels in advance to serve as basic data for recommendation, a recommended content alternative pool is obtained through the corresponding relation between the user label data and the recommended content label data which are established in advance, and the recommended information meeting the actual requirements of the users can be further calculated from the content alternative pool based on the preference selection of the users, so that a systematic solution including courses, teaching aids and assessment is obtained according to the comprehensive information of the family conditions of the users, the development conditions of the infants and the like.
Example two:
referring to fig. 4, a second embodiment of the present invention provides an information recommendation apparatus, including: an acquisition unit 1, a processing unit 2, a reading unit 3, a calculation unit 4, a rough matching unit 5, a fine matching unit 6, and an output unit 7.
The acquiring unit 1 is configured to acquire user-related data.
The acquiring of the user related data specifically comprises: information related to the user and information related to the infant are obtained.
The acquiring information related to the user and information related to the infant specifically includes:
acquiring infant ability development data information input by a user for answering a test question, wherein the infant ability development data information comprises family basic information of the user;
acquiring information related to a user and an infant, wherein the information is communicated between after-sales service personnel and the user;
acquiring behavior data of a user on the network or an application program, and capturing and recording the behavior data through a data embedded point system on the network or the application program;
and acquiring product use feedback information or product use evaluation information submitted by a user on the equipment.
The behavior of the user such as class, card punching and the like is recorded by the system.
The processing unit 2 is configured to process the acquired user-related data to obtain user standardized data.
The step of processing the acquired user-related data to acquire user standardized data specifically includes: mapping the obtained user-related data to user standardized data based on a built-in data dictionary.
For data such as data which is automatically input by a user and acquired by the acquisition unit 1 or behavior data generated when the terminal device is operated, the system maps the data to a data dictionary to obtain a standardized numerical value, and after the process is finished, the data stored in the system is not scattered and scattered any more for subsequent use.
In the system of the invention, a data dictionary is pre-built, and for each user-related data, the data dictionary has corresponding numerical values corresponding to the numerical values, so that each user-related data can be mapped into corresponding user standardized numerical values by using the data dictionary. For example, for the gender of a baby, in the data dictionary, 1 is used to represent a male baby, 2 is used to represent a female baby, and 0 is used to represent unknown.
The reading unit 3 is used for reading data in the database.
Referring to fig. 2, the data in the database includes the following tag data:
(1) the content tag data is recommended.
Referring to fig. 3, the recommended content label data includes an infant ability label, a teaching aid label, a course content label, and a nursery information content label. The tags may be entered by a professional learner.
Where each of the tags can be ranked, the system defines nine large dimensions and about 60 fine categories under the nine large dimensions, and hundreds of capability points under the fine categories. For example, the three-layer label of ' whole brain thinking ' -sensory perception development-tactile experience ' represents the ability of ' tactile experience ' in the subdivision category of ' whole brain thinking ' of the infant in the large development education direction of ' whole brain thinking '.
(2) User tag data.
The user tag data includes user attribute tags and user behavior tags. The method specifically comprises the following steps:
a) the user attribute tags comprise a month age tag of a baby of the user, a gender tag of the baby, a child condition tag of a family of the user (such as full-time mothers and mothers, old people's help belts and the like), and the like;
b) user's action label, including using habit time interval distribution label, teaching aid preference label, course preference label etc..
(3) Relationship data of the user tag data and the user standardized data.
The system accumulates relational data of user tags and user standardized data based on historical users.
(4) And relationship data of the user tag data and the recommended content tag data.
This part of the data is an accumulation of data generated by the following rough matching unit 5 and precise matching unit 6, and the relationship data of the user tag data and the recommended content tag data is continuously accumulated by a machine learning algorithm.
The calculating unit 4 is configured to calculate the obtained user standardized data into user tag data based on the relationship data between the read user tag data and the user standardized data in the database.
In the process of system label classification, different calculation modes are adopted for different label types. For example, for the month age and sex labels, the system adopts an information collection and classification mode; for the use habit label, the system adopts a calculation mode of statistical distribution; and for the preference label of the teaching aid, the system adopts a machine learning calculation mode based on a KNN algorithm.
The rough matching unit 5 is configured to perform rough matching on the user tag data to obtain a recommended content alternative pool preliminarily recommended to the user.
The recommended content alternative pool comprises infant ability reports, teaching aids, course contents, infant care information contents and the like.
The step of roughly matching the user tag data to obtain a recommended content alternative pool preliminarily recommended to the user specifically includes: and based on the relation data of the user tag data and the recommended content tag data in the read database, roughly matching the user tag data to obtain a recommended content alternative pool which is preliminarily recommended to the user. The system preliminarily calculates a recommended content alternative pool recommended to the user based on the user tag data calculated in the calculating unit 4 and the relation data of the user tag data and the recommended content tag data read by the reading unit 3.
In the calculation process of the part, the system adopts various machine learning algorithms in advance, including but not limited to collaborative filtering and Knowledge-based recommendation algorithms (Knowledge-based Recommendations), and the recommended contents are sorted according to relevance.
And the accurate matching unit 6 is configured to send the recommended content alternative pool to the user, and perform accurate matching based on the preference of the user to obtain the final recommended content.
The final recommended content comprises infant ability development reports, teaching aids, course content, infant care information content and the like.
The amount of recommended contents which can be obtained by the end user is larger than the amount of recommended contents which can be obtained by the end user in the alternative pool calculated by the rough matching unit 5, and the recommended contents may be different from the contents which the user wants to know, so that the system does not accord with the final expectation of the user, the system gives the user a positive selection opportunity in the process, for example, the user can avoid obtaining a previously learned course or a teaching aid already existing at home by replacing a professional teaching aid or a course, or receives subjective preference input by the user, and the final recommended contents are further calculated based on the subjective preference of the user.
The output unit 7 is configured to send the final recommended content to a client.
By means of the information recommendation device in the embodiment, various information about users and infants can be acquired from various channels in advance to serve as basic data for recommendation, a recommended content alternative pool is obtained through the corresponding relation between the user label data and the recommended content label data which are established in advance, and the recommended information meeting the actual requirements of the users can be further calculated from the content alternative pool based on the preference selection of the users, so that a systematic solution including courses, teaching aids and assessment is obtained according to the comprehensive information of the family conditions of the users, the development conditions of the infants and the like.
Example three:
referring to fig. 5, a third embodiment of the present invention further provides a client, where the client is the client in the second embodiment, and the client includes an input unit 8, an output unit 9, a receiving unit 10, and a sending unit 11.
The input unit 8 is used for inputting information related to the user and information related to the infant.
The input information includes:
the user answers the infant ability development data information input by the test questions, wherein the infant ability development data information comprises the family basic information of the user;
information related to the user and the infant that the user and the after-sales service personnel communicate with;
the method comprises the steps that information input by a user when the user uses a network or an application program is triggered to generate behavior data of the user, and the behavior data is captured and recorded through a data embedded point system on the network or the application program;
product usage feedback information or product usage rating information submitted by a user on the device.
The input unit 8 is further configured to input preference information of the user after the receiving unit receives the recommended content alternative pool preliminarily recommended to the user.
The receiving unit 10 is configured to receive a recommended content alternative pool preliminarily recommended to the user and a final recommended content from the information recommendation device.
The sending unit 11 is configured to send information related to the user, information related to the infant, and preference information of the user to the information recommendation device.
The output unit 9 is configured to output the recommended content alternative pool preliminarily recommended to the user, and output the final recommended content.
Example four:
referring to fig. 6, a fourth embodiment of the present invention provides an information recommendation system, including: a client 12, an information recommendation device 13 and a database 14.
The module of the client 12 constitutes the client in the third embodiment, the module of the information recommendation device 13 constitutes the information recommendation device in the second embodiment, and the data stored in the database 14 is the data stored in the databases in the first and second embodiments.
The information recommendation device 13 is configured to obtain user-related data, process the user-related data to obtain user standardized data, calculate the user standardized data into user tag data based on relationship data between the user tag data read from the database and the user standardized data, perform rough matching on the user tag data to obtain a recommended content alternative pool preliminarily recommended to a user, send the recommended content alternative pool to the client, perform precise matching based on user preferences to obtain final recommended content, and send the final recommended content to the client 12.
The database 14 stores relationship data between the user tag data and the user standardized data and relationship data between the user tag data and the recommended content tag data.
The client receives the recommended content alternative pool sent by the information recommendation device 13, sends the user preference to the information recommendation device 13, and receives the final recommended content.
The step of roughly matching the user tag data to obtain a recommended content alternative pool preliminarily recommended to the user specifically comprises: and based on the read relation data between the user tag data and the recommended content tag data in the database system, roughly matching the user tag data to obtain a recommended content alternative pool which is preliminarily recommended to the user.
In addition, other embodiments of the present invention also provide an electronic device, which includes a storage device and one or more processors, wherein the storage device is used for storing one or more programs, and when the one or more programs are executed by the one or more processors, the one or more processors implement the method according to the first embodiment.
Other embodiments of the present invention also disclose a computer-readable storage medium having a computer program stored thereon, which, when executed, implements the method of embodiment one.
Other embodiments of the invention also disclose a computer program which, when executed, implements the method of embodiment one.
It is noted that parts of the present invention may be applied as a computer program product, such as computer program instructions, which, when executed by an intelligent electronic device (such as a smart mobile phone or a tablet computer, etc.), may invoke or provide the method and/or solution according to the present invention through the operation of the intelligent electronic device. Program instructions which invoke the methods of the present invention may be stored on a fixed or removable recording medium and/or transmitted via a data stream over a broadcast or other signal-bearing medium and/or stored in a working memory of an intelligent electronic device operating in accordance with the program instructions. An embodiment according to the invention herein comprises an apparatus comprising a memory for storing computer program instructions and a processor for executing the program instructions, wherein the computer program instructions, when executed by the processor, trigger the apparatus to perform a method and/or solution according to embodiments of the invention as described above.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description of the embodiments, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned. Furthermore, it is obvious that the word "comprising" does not exclude other elements or steps, and the singular does not exclude the plural. A plurality of units or means recited in the system claims may also be implemented by one unit or means in software or hardware. The terms first, second, etc. are used to denote names, but not any particular order.
Claims (23)
1. An information recommendation method, characterized in that the method comprises the steps of:
s1: acquiring user related data;
s2: processing the acquired user related data to obtain user standardized data;
s3: reading data in a database;
s4: calculating the obtained user standardized data into user tag data based on the relationship data of the user tag data and the user standardized data in the read database;
s5: roughly matching the user tag data to obtain a recommended content alternative pool which is preliminarily recommended to the user;
s6: sending the recommended content alternative pool to a user, and performing accurate matching based on the preference of the user to obtain final recommended content;
s7: and sending the final recommended content to a client.
2. The information recommendation method according to claim 1, wherein the obtaining of the user-related data specifically comprises: information related to the user and information related to the infant are obtained.
3. The information recommendation method according to claim 2, wherein the acquiring information related to the user and information related to the infant specifically comprises:
acquiring infant ability development data information input by a user for answering a test question, wherein the infant ability development data information comprises family basic information of the user;
acquiring information related to a user and an infant, wherein the information is communicated between after-sales service personnel and the user;
acquiring behavior data of a user on a network or an application program;
and acquiring product use feedback information or product use evaluation information submitted by a user on the equipment.
4. The information recommendation method according to any of claims 1 to 3, wherein the processing the obtained user-related data to obtain user-standardized data is specifically: mapping the obtained user-related data to user standardized data based on a built-in data dictionary.
5. The information recommendation method according to claim 1, wherein the data in the database comprises:
(1) recommending content tag data;
(2) user tag data;
(3) relational data of user tag data and user standardized data;
(4) and relationship data of the user tag data and the recommended content tag data.
6. The information recommendation method according to claim 5, wherein the recommendation content label data includes an infant ability label, a teaching aid label, a course content label, and a nursing information content label.
7. The information recommendation method according to claim 5 or 6, wherein the user tag data includes a user attribute tag and a user behavior tag.
8. The information recommendation method according to claim 1, wherein the step of roughly matching the user tag data to obtain a recommended content alternative pool preliminarily recommended to the user specifically comprises:
and based on the relation data of the user tag data and the recommended content tag data in the read database, roughly matching the user tag data to obtain a recommended content alternative pool which is preliminarily recommended to the user.
9. The information recommendation method according to claim 1, wherein the final recommendation content includes an infant and child ability report, a teaching aid content, a course content, and a child care information content.
10. An information recommendation apparatus, characterized in that the apparatus comprises: the device comprises an acquisition unit, a processing unit, a reading unit, a calculation unit, a rough matching unit, an accurate matching unit and an output unit;
the acquisition unit is used for acquiring user related data;
the processing unit is used for processing the acquired user related data to acquire user standardized data;
the reading unit is used for reading data in a database;
the calculation unit is used for calculating the obtained user standardized data into user tag data based on the relationship data of the user tag data and the user standardized data in the read database;
the rough matching unit is used for roughly matching the user tag data to obtain a recommended content alternative pool which is preliminarily recommended to the user;
the accurate matching unit is used for sending the recommended content alternative pool to the user and carrying out accurate matching based on the preference of the user to obtain the final recommended content;
and the output unit is used for sending the final recommended content to a client.
11. The information recommendation device according to claim 10, wherein the obtaining of the user-related data specifically comprises: information related to the user and information related to the infant are obtained.
12. The information recommendation device according to claim 11, wherein the acquiring information related to the user and information related to the infant specifically comprises:
acquiring infant ability development data information input by a user for answering a test question, wherein the infant ability development data information comprises family basic information of the user;
acquiring information related to a user and an infant, wherein the information is communicated between after-sales service personnel and the user;
acquiring behavior data of a user on a network or an application program;
and acquiring product use feedback information or product use evaluation information submitted by a user on the equipment.
13. The information recommendation device according to any of claims 10-12, wherein said processing the obtained user related data to obtain user standardized data is specifically: mapping the obtained user-related data to user standardized data based on a built-in data dictionary.
14. The information recommendation device according to claim 10, wherein the data in the database comprises:
(1) recommending content tag data;
(2) user tag data;
(3) relational data of user tag data and user standardized data;
(4) and relationship data of the user tag data and the recommended content tag data.
15. The information recommendation device according to claim 14, wherein the recommendation content label data includes an infant ability label, a teaching aid label, a course content label, and a nursing information content label.
16. The information recommendation device according to claim 14 or 15, wherein the user tag data comprises a user attribute tag and a user behavior tag.
17. The information recommendation device according to claim 10, wherein the rough matching of the user tag data to obtain the candidate pool of recommended content to be preliminarily recommended to the user is specifically:
and based on the relation data of the user tag data and the recommended content tag data in the read database, roughly matching the user tag data to obtain a recommended content alternative pool which is preliminarily recommended to the user.
18. The information recommendation device according to claim 10, wherein the final recommendation content includes an infant and child ability report, a teaching aid content, a course content, a child care information content.
19. An information recommendation system, characterized in that the information recommendation system comprises: the system comprises a client, an information recommendation device and a database;
the information recommendation device is used for acquiring user related data, processing the user related data to acquire user standardized data, calculating the user standardized data into user tag data based on the relation data of the user tag data read from the database and the user standardized data, roughly matching the user tag data to acquire a recommended content alternative pool preliminarily recommended to a user, sending the recommended content alternative pool to the client, accurately matching based on user preference to acquire final recommended content, and sending the final recommended content to the client;
the database stores the relation data of the user tag data and the user standardized data and the relation data of the user tag data and the recommended content tag data;
and the client receives the recommended content alternative pool sent by the information recommending device, sends the preference of the user to the information recommending device, and receives the final recommended content.
20. The information recommendation system according to claim 19, wherein the rough matching of the user tag data to obtain the candidate pool of recommended content to be preliminarily recommended to the user is specifically: and based on the read relation data between the user tag data and the recommended content tag data in the database system, roughly matching the user tag data to obtain a recommended content alternative pool which is preliminarily recommended to the user.
21. An electronic device, characterized in that the device comprises:
a storage device;
one or more processors;
wherein the storage device is configured to store one or more programs which, when executed by the one or more processors, cause the one or more processors to implement an information recommendation method as claimed in any one of claims 1-9.
22. A computer-readable storage medium on which a computer program is stored which, when executed, implements an information recommendation method as claimed in any one of claims 1-9.
23. A computer program product comprising computer program instructions for implementing an information recommendation method as claimed in any one of claims 1-9 when executed by a processor.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113626575A (en) * | 2021-09-01 | 2021-11-09 | 浙江力石科技股份有限公司 | Intelligent recommendation method based on user question answering |
| CN114662920A (en) * | 2022-03-24 | 2022-06-24 | 中国工商银行股份有限公司 | Course pushing method, device, computer equipment, storage medium and program product |
Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106777132A (en) * | 2016-12-18 | 2017-05-31 | 深圳市辣妈帮科技有限公司 | Data processing method and device |
| CN107222557A (en) * | 2017-06-27 | 2017-09-29 | 乐视致新电子科技（天津）有限公司 | Information recommendation method, device and server |
| CN108134955A (en) * | 2016-12-01 | 2018-06-08 | 深圳市优朋普乐传媒发展有限公司 | A kind of method and device of program sequence |
| CN108376339A (en) * | 2017-09-11 | 2018-08-07 | 加拿大辉莱广告公司 | Advertising specialty recommends method and device |
| CN108765076A (en) * | 2018-05-24 | 2018-11-06 | 蜜芽宝贝（北京）网络科技有限公司 | Mother and baby's content recommendation method, device and readable storage medium storing program for executing |
| CN109118336A (en) * | 2018-08-24 | 2019-01-01 | 平安科技（深圳）有限公司 | Information recommendation method, device, computer equipment and storage medium |
| CN109408720A (en) * | 2018-09-16 | 2019-03-01 | 苏州云巢文化传媒有限公司 | Information recommendation method, system and medium based on electronics lock screen |
| CN109919652A (en) * | 2019-01-17 | 2019-06-21 | 平安城市建设科技(深圳)有限公司 | User group's classification method, device, equipment and storage medium |
| CN110059249A (en) * | 2019-04-03 | 2019-07-26 | 华为技术有限公司 | Method, terminal device and the system of personalized recommendation |
| CN110909237A (en) * | 2019-10-24 | 2020-03-24 | 泰康保险集团股份有限公司 | Method, device, equipment and computer readable medium for recommending content |
| CN111539787A (en) * | 2020-04-15 | 2020-08-14 | 上海明略人工智能(集团)有限公司 | Information recommendation method, intelligent glasses, storage medium and electronic device |
-
2020
- 2020-11-30 CN CN202011368200.4A patent/CN112506975A/en active Pending
Patent Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108134955A (en) * | 2016-12-01 | 2018-06-08 | 深圳市优朋普乐传媒发展有限公司 | A kind of method and device of program sequence |
| CN106777132A (en) * | 2016-12-18 | 2017-05-31 | 深圳市辣妈帮科技有限公司 | Data processing method and device |
| CN107222557A (en) * | 2017-06-27 | 2017-09-29 | 乐视致新电子科技（天津）有限公司 | Information recommendation method, device and server |
| CN108376339A (en) * | 2017-09-11 | 2018-08-07 | 加拿大辉莱广告公司 | Advertising specialty recommends method and device |
| CN108765076A (en) * | 2018-05-24 | 2018-11-06 | 蜜芽宝贝（北京）网络科技有限公司 | Mother and baby's content recommendation method, device and readable storage medium storing program for executing |
| CN109118336A (en) * | 2018-08-24 | 2019-01-01 | 平安科技（深圳）有限公司 | Information recommendation method, device, computer equipment and storage medium |
| CN109408720A (en) * | 2018-09-16 | 2019-03-01 | 苏州云巢文化传媒有限公司 | Information recommendation method, system and medium based on electronics lock screen |
| CN109919652A (en) * | 2019-01-17 | 2019-06-21 | 平安城市建设科技(深圳)有限公司 | User group's classification method, device, equipment and storage medium |
| CN110059249A (en) * | 2019-04-03 | 2019-07-26 | 华为技术有限公司 | Method, terminal device and the system of personalized recommendation |
| CN110909237A (en) * | 2019-10-24 | 2020-03-24 | 泰康保险集团股份有限公司 | Method, device, equipment and computer readable medium for recommending content |
| CN111539787A (en) * | 2020-04-15 | 2020-08-14 | 上海明略人工智能(集团)有限公司 | Information recommendation method, intelligent glasses, storage medium and electronic device |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113626575A (en) * | 2021-09-01 | 2021-11-09 | 浙江力石科技股份有限公司 | Intelligent recommendation method based on user question answering |
| CN114662920A (en) * | 2022-03-24 | 2022-06-24 | 中国工商银行股份有限公司 | Course pushing method, device, computer equipment, storage medium and program product |
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